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Competitive facility location and design with reactions of competitors already in the market
Saidani N., Chu F., Chen H.
European Journal of Operational Research 219, 1 (2012) 9--17 - http://hal.archives-ouvertes.fr/hal-00690795
Articles dans des revues avec comité de lecture
Competitive facility location and design with reactions of competitors already in the market
Nasreddine Saidani ( ) 1, Feng Chu () 2, Haoxun Chen () 1
1 :  Institut Charles Delaunay
Université de Technologie de Troyes : 2848
France
2 :  Informatique, Biologie Intégrative et Systèmes Complexes (IBISC)
http://www.ibisc.univ-evry.fr/
Université d'Evry-Val d'Essonne : EA4526
40 rue du Pelvoux, Courcouronnes ; 91020 Evry Cedex
France
AROBAS
LOSI
A new retail facility is to locate and its service quality is to determine where similar facilities of competitors offering the same goods are already present. The market share captured by each facility depends on its distance to customers and its quality, which is described by a probabilistic Huff-like model. In order to maximize the profit of the new facility, a two-stage method is developed, which takes into account the reactions of the competitors. In the quality decision stage, the competitive decision process occurring among facilities is modelled as a game, whose solution is given by its Nash equilibrium. The solution, which can be represented as functions of the location of the new facility, is obtained by analytical resolution of a system of equations in the case of one facility in the market or by polynomial approximation in the case of multiple facilities. In the location decision stage, an interval based global optimization method is used to determine the best location of the new facility. Numerical experiments on randomly generated instances demonstrate the effectiveness of the method.
Anglais

European Journal of Operational Research
Publisher Elsevier
ISSN 0377-2217 
internationale
16/05/2012
219
1
9--17

Competitive facility location and design – Nash equilibrium – Function approximation – Global optimization – Branch-and-bound